Tables and figures for publications
Alberto Garcia-Basteiro and Joe Brew
March 28, 2017
Tables
Descriptive tables
Global differences by:, age, sex, world region
| IHME | WHO | Dif | % difference (WHO / IHME) | % difference (IHME / WHO) | ||
|---|---|---|---|---|---|---|
| Total | HIV+TB only | 211604 | 389042 | 177438.13321 | 83.85% | -45.61% |
| TB only | 1111312 | 1379440 | 268128.44955 | 24.13% | -19.44% | |
| Total TB | 1322916 | 1768482 | 445566.58275 | 33.68% | -25.19% | |
| Adults | HIV+TB only | 177567 | 348026 | 170458.90473 | 96% | -48.98% |
| TB only | 1075691 | 1210620 | 134929.12946 | 12.54% | -11.15% | |
| Total TB | 1253257 | 1558645 | 305388.03419 | 24.37% | -19.59% | |
| Children | HIV+TB only | 34037 | 41016 | 6979.22848 | 20.5% | -17.02% |
| TB only | 35621 | 168821 | 133199.32009 | 373.93% | -78.9% | |
| Total TB | 69659 | 209837 | 140178.54857 | 201.24% | -66.8% | |
| Female | HIV+TB only | 78110 | 143496 | 65386.51804 | 83.71% | -45.57% |
| TB only | 367764 | 352488 | 15276.43876 | -4.15% | 4.33% | |
| Total TB | 445874 | 495984 | 50110.07929 | 11.24% | -10.1% | |
| Male | HIV+TB only | 99457 | 204471 | 105013.90757 | 105.59% | -51.36% |
| TB only | 707927 | 858132 | 150205.56821 | 21.22% | -17.5% | |
| Total TB | 807383 | 1062603 | 255219.47578 | 31.61% | -24.02% | |
| AMR | HIV+TB only | 579 | 620 | 41.31917 | 7.14% | -6.66% |
| TB only | 2036 | 1914 | 122.37010 | -6.01% | 6.39% | |
| Total TB | 2615 | 2534 | 81.05093 | -3.1% | 3.2% | |
| EMR | HIV+TB only | 165 | 533 | 368.30203 | 223.05% | -69.04% |
| TB only | 14658 | 14572 | 85.51575 | -0.58% | 0.59% | |
| Total TB | 14823 | 15106 | 282.78629 | 1.91% | -1.87% | |
| EUR | HIV+TB only | 212 | 374 | 161.68749 | 76.15% | -43.23% |
| TB only | 2383 | 2999 | 615.93832 | 25.85% | -20.54% | |
| Total TB | 2595 | 3373 | 777.62581 | 29.97% | -23.06% | |
| SEA | HIV+TB only | 19310 | 28870 | 9560.04060 | 49.51% | -33.11% |
| TB only | 333250 | 345889 | 12639.43214 | 3.79% | -3.65% | |
| Total TB | 352560 | 374759 | 22199.47275 | 6.3% | -5.92% | |
| WPR | HIV+TB only | 2057 | 2010 | 47.13348 | -2.29% | 2.35% |
| TB only | 39055 | 28351 | 10704.20283 | -27.41% | 37.76% | |
| Total TB | 41112 | 30361 | 10751.33632 | -26.15% | 35.41% |
Analytical tables
Table with model output for estimating likelihood or magnitude of difference in estimates by HIV, age, sex, and region.
This section is unfinished.
Graphs
Descriptive graphs
Standardized difference
Rankings of highest absolute and standardized differences for IHME and WHO.
Standardized difference for incidence
AB metric (a-b) / (a+b)
Rankings of highest absolute and standardized differences for IHME and WHO.
Maps
The below scatterplot shows the correlation between WHO (x-axis) estimates and IHME (y-axis) estimates, with each point colored by its (WHO-defined) region.
Analytical for adjusted_stand_dif
In the following four charts, Libya has been excluded as an outlier.
a) HIV prevalence among TB cases
b) Rifampicine resistance (MDR prevalence)
b.i) Rifampicine resistance (MDR prevalence using reported cases)
c) CDR (case detection rate per WHO)
c.i) CDR (case detection rate per IHME)
c.ii) Correlation between different case detections rates
d) CFR (case fatality rate)
Prevalence survey’s effect
Association of prevalence survey and standardized difference
# A tibble: 2 x 2
prevsurvey median_adjusted_stand_dif
<dbl> <dbl>
1 0 - 5.20
2 1.00 16.5 Analytical for stand_dif_inc_adj
a) HIV prevalence among TB cases
b) Rifampicine resistance (MDR prevalence)
b.i) Rifampicine resistance (MDR prevalence using reported cases)
c) CDR (case detection rate per WHO)
Error: <text>:16:0: unexpected end of input
14: # Those countries with low case detection (oftentimes have prev survey),
15: # WHO estimates more deaths than IHME
^c.i) CDR (case detection rate per IHME)
d) CFR (case fatality rate)
Prevalence survey’s effect
Association of prevalence survey and standardized difference
Analytical for adjusted_ab
a) HIV prevalence among TB cases
b) Rifampicine resistance (MDR prevalence)
b.i) Rifampicine resistance (MDR prevalence using reported cases)
c) CDR (case detection rate per WHO)
c.i) CDR (case detection rate per IHME)
d) CFR (case fatality rate)
Prevalence survey’s effect
Association of prevalence survey and standardized difference
Modeling
Linear regression to estimate effect of prevalence survey on absolute difference in cases (WHO minus IHME), adjusting for region.
95% confidence intervals
Linear regression to estimate effect of prevalence survey on adjusted standardized difference in cases, adjusting for region.
95% confidence intervals
Interactive map
Relative difference (function of maximum estimate)
(Unfinished)
Map of world
Map of Mozambique
Correlation coefficients
Correlation of adjusted stand diff with a) HIV prevalence, CDR by both, CFR, MDR prevalence.
cor(df$adjusted_stand_dif, df$newrel_hivpos, use = 'complete.obs')[1] 0.09204849cor(df$adjusted_stand_dif, df$gb_c_cdr, use = 'complete.obs')[1] -0.3688292cor(df$adjusted_stand_dif, df$cdr_ihme, use = 'complete.obs')[1] 0.4355758cor(df$adjusted_stand_dif, df$case_fatality_rate_2014, use = 'complete.obs')[1] -0.1353054cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014, use = 'complete.obs')[1] -0.1171681cor(df$adjusted_stand_dif, df$case_fatality_rate_2015, use = 'complete.obs')[1] -0.1586533cor(df$adjusted_stand_dif, df$case_fatality_rate_2014_new, use = 'complete.obs')[1] -0.1587409cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014_new, use = 'complete.obs')[1] -0.1147299cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_new, use = 'complete.obs')[1] -0.1760415cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_adjusted, use = 'complete.obs')[1] -0.1825428cor(df$adjusted_stand_dif, df$p_mdr_new, use = 'complete.obs')[1] 0.05559062cor(df$adjusted_stand_dif, df$reported_mdr, use = 'complete.obs')[1] -0.0131539Hypothesis testing on prevalence survey
Does region affect likelihood of having a prevalence survey?
xt <- table(df$prevsurvey, df$who_region)
xt
AFR AMR EMR EUR SEA WPR
0 37 37 20 52 8 22
1 10 0 2 0 3 4chisq.test(xt)
Pearson's Chi-squared test
data: xt
X-squared = 21.511, df = 5, p-value = 0.0006482Does having a prev survey affect the adjusted stand diff?
t.test(x = df$adjusted_stand_dif[df$prevsurvey == 0],
y = df$adjusted_stand_dif[df$prevsurvey == 1])
Welch Two Sample t-test
data: df$adjusted_stand_dif[df$prevsurvey == 0] and df$adjusted_stand_dif[df$prevsurvey == 1]
t = -2.1643, df = 21.066, p-value = 0.04207
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-39.2150725 -0.7865973
sample estimates:
mean of x mean of y
-4.545393 15.455442 Supplemental charts
April 18, 2018
Alberto Method: top and bottom 10 stand diff (children only)
Martien Method: top and bottom 10 stand diff (children only)
Concordance between adult and child measures (Alberto method)
Concordance between adult and child measures (Martien method)
Alberto Method: top and bottom 10 stand diff (HIV only, all ages)
Martien Method: top and bottom 10 stand diff (HIV, all ages)
Effect of prevalence survey
Is CDR associated with stand diff in the linear regression among those countries without prevalence survey?
x <- df
x$gb_c_cdr[!is.finite(x$gb_c_cdr)] <- NA
x$original_stand_dif[!is.finite(x$original_stand_dif)] <- NA
fit <- lm(gb_c_cdr ~ original_stand_dif, data = x[x$prevsurvey == 0,])
summary(fit)
Call:
lm(formula = gb_c_cdr ~ original_stand_dif, data = x[x$prevsurvey ==
0, ])
Residuals:
Min 1Q Median 3Q Max
-42.50 -12.33 4.16 10.80 23.21
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.0320 1.1661 65.201 < 0.0000000000000002 ***
original_stand_dif -0.3555 0.1123 -3.166 0.00187 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.35 on 151 degrees of freedom
(23 observations deleted due to missingness)
Multiple R-squared: 0.06226, Adjusted R-squared: 0.05605
F-statistic: 10.03 on 1 and 151 DF, p-value: 0.001868Yes. Among countries without a prevalence survey, a 1 unit increase in the standardized difference is associated with a 0.355 unit decrease in the CDR. This is significant (p < 0.01).
More visualizations
Afternoon of Wednesday, April 19. All charts are WHO minus IHME
a) absch: w_both_014_tbtotal_nd - i_both_014_tbtotal_nd
| Country | Value |
|---|---|
| India | 49508.8443 |
| Nigeria | 32003.9550 |
| Indonesia | 12752.4252 |
| Bangladesh | 8616.7562 |
| Tanzania | 5992.6238 |
| Dem. Rep. Congo | 5147.3671 |
| China | 3214.4091 |
| Myanmar | 3169.4789 |
| Pakistan | 2334.5255 |
| Angola | 2089.3906 |
| Zimbabwe | -2191.6025 |
| South Africa | -1524.9994 |
| Malawi | -1022.3595 |
| Uganda | -504.5276 |
| Burkina Faso | -499.9316 |
| Lesotho | -415.1728 |
| Rwanda | -378.9136 |
| Burundi | -279.2630 |
| Swaziland | -223.2993 |
| Namibia | -170.1219 |
standardized
| Country | Value |
|---|---|
| North Korea | 100.00000 |
| Bangladesh | 99.41480 |
| Timor-Leste | 99.01265 |
| Papua New Guinea | 98.80690 |
| Libya | 97.09310 |
| Viet Nam | 97.08583 |
| Thailand | 95.00197 |
| United Arab Emirates | 94.13214 |
| Haiti | 93.15330 |
| Myanmar | 92.41885 |
| Rwanda | -68.52748 |
| Zimbabwe | -55.26266 |
| Azerbaijan | -53.23740 |
| Burkina Faso | -49.28143 |
| Japan | -47.64091 |
| Swaziland | -40.43862 |
| Honduras | -36.12995 |
| Namibia | -33.76727 |
| Malawi | -30.59634 |
| Eritrea | -30.44260 |
Downloadable table with full data
b) absad: (w_f_15plus_htb_nd + w_m_15plus_htb_nd + w_f_15plus_tb_nd + w_m_15plus_tb_nd) - ( i_both_15plus_tb_nd + i_both_15plus_htb_nd)
| Country | Value |
|---|---|
| Nigeria | 184617.353 |
| Bangladesh | 41246.468 |
| Tanzania | 32279.694 |
| South Africa | 30633.208 |
| Mozambique | 27458.985 |
| Dem. Rep. Congo | 20863.048 |
| Indonesia | 13369.005 |
| North Korea | 11553.577 |
| Ghana | 8202.510 |
| Madagascar | 7978.496 |
| India | -28812.449 |
| Ethiopia | -22502.583 |
| China | -16752.815 |
| Philippines | -9655.750 |
| Zimbabwe | -8890.743 |
| Nepal | -5859.519 |
| Viet Nam | -4999.429 |
| Uganda | -4576.158 |
| Burkina Faso | -4337.049 |
| Niger | -3617.583 |
standardized
| Country | Value |
|---|---|
| Nigeria | 97.94014 |
| Marshall Islands | 88.17223 |
| Timor-Leste | 87.20306 |
| Papua New Guinea | 85.49242 |
| Libya | 85.13396 |
| North Korea | 84.44479 |
| Greenland | 77.69338 |
| Iceland | 76.08632 |
| Sudan | 66.45726 |
| Congo | 65.73305 |
| Azerbaijan | -100.00000 |
| Egypt | -86.46321 |
| Macedonia | -83.75589 |
| Saint Lucia | -80.58197 |
| Rwanda | -78.99093 |
| Syrian Arab Republic | -71.15123 |
| Burkina Faso | -67.09132 |
| Comoros | -67.08868 |
| Eritrea | -66.89072 |
| Honduras | -65.43858 |
Downloadable table with full data
c) abshiv: w_both_all_htb_nd - i_both_all_htb_nd
| Country | Value |
|---|---|
| Nigeria | 52805.3129 |
| South Africa | 29593.6434 |
| Indonesia | 19480.6093 |
| Tanzania | 18779.4125 |
| Mozambique | 17870.0413 |
| India | 9962.0335 |
| Dem. Rep. Congo | 9497.6106 |
| Zambia | 7498.0137 |
| Angola | 5670.8262 |
| Ghana | 3740.3204 |
| Zimbabwe | -6432.0229 |
| Ethiopia | -4338.3535 |
| Uganda | -2717.5779 |
| Botswana | -1463.5821 |
| Namibia | -944.1877 |
| Viet Nam | -920.1390 |
| Rwanda | -748.9198 |
| Swaziland | -744.3264 |
| Burkina Faso | -738.9729 |
| Burundi | -640.2910 |
standardized
| Country | Value |
|---|---|
| Madagascar | 96.74147 |
| Bhutan | 93.36600 |
| Afghanistan | 93.35732 |
| Moldova Republic of | 88.93088 |
| Nigeria | 85.30343 |
| Cape Verde | 82.80465 |
| Papua New Guinea | 78.06040 |
| Uzbekistan | 77.99681 |
| Congo | 76.74764 |
| Mauritius | 73.54996 |
| Turkmenistan | -100.00000 |
| Chile | -82.69580 |
| Argentina | -73.74461 |
| Puerto Rico | -68.08163 |
| Japan | -66.53380 |
| North Korea | -58.28064 |
| Serbia | -57.62913 |
| Rwanda | -57.54725 |
| Burkina Faso | -57.53504 |
| Iran Islamic Republic of | -55.40047 |
Downloadable table with full data
d) abstb: w_both_all_tb_nd - i_both_all_tb_nd
| Country | Value |
|---|---|
| Nigeria | 163815.995 |
| Bangladesh | 49751.746 |
| Tanzania | 19492.905 |
| Dem. Rep. Congo | 16512.804 |
| North Korea | 13321.155 |
| Mozambique | 11039.108 |
| India | 10734.362 |
| Madagascar | 8024.573 |
| Afghanistan | 7421.693 |
| Indonesia | 6640.821 |
| Ethiopia | -18311.726 |
| China | -13512.763 |
| Philippines | -9788.048 |
| Nepal | -5666.740 |
| Zimbabwe | -4650.323 |
| Burkina Faso | -4098.008 |
| Niger | -3269.260 |
| Senegal | -2887.683 |
| Uganda | -2363.108 |
| Viet Nam | -2331.514 |
standardized
| Country | Value |
|---|---|
| Nigeria | 93.09254 |
| Marshall Islands | 86.33247 |
| Timor-Leste | 86.30530 |
| Libya | 86.10756 |
| North Korea | 84.53367 |
| Papua New Guinea | 84.33227 |
| Greenland | 74.17450 |
| Gabon | 73.73470 |
| Iceland | 73.35414 |
| Sudan | 66.26216 |
| Azerbaijan | -100.00000 |
| Macedonia | -79.75107 |
| Rwanda | -79.58858 |
| Honduras | -76.10209 |
| Bahamas | -72.93482 |
| Saint Lucia | -72.87303 |
| Egypt | -67.49221 |
| Zimbabwe | -64.95982 |
| Burkina Faso | -62.26468 |
| Kuwait | -61.81510 |
Downloadable table with full data
e) absall: w_both_all_tbtotal_nd - i_both_all_tbtotal_nd
| Country | Value |
|---|---|
| Nigeria | 216621.308 |
| Bangladesh | 49863.225 |
| Tanzania | 38272.318 |
| South Africa | 29108.209 |
| Mozambique | 28909.149 |
| Indonesia | 26121.431 |
| Dem. Rep. Congo | 26010.415 |
| India | 20696.396 |
| North Korea | 13218.478 |
| Angola | 9910.123 |
| Ethiopia | -22650.079 |
| China | -13538.406 |
| Zimbabwe | -11082.346 |
| Philippines | -9435.607 |
| Nepal | -5476.979 |
| Uganda | -5080.686 |
| Burkina Faso | -4836.981 |
| Niger | -3757.964 |
| Viet Nam | -3251.653 |
| Senegal | -3147.204 |
standardized
| Country | Value |
|---|---|
| Nigeria | 98.95851 |
| Marshall Islands | 91.15784 |
| Timor-Leste | 90.95049 |
| Papua New Guinea | 89.67219 |
| Libya | 89.20694 |
| North Korea | 87.77826 |
| Greenland | 77.96434 |
| Iceland | 76.32096 |
| Sudan | 69.42114 |
| Laos | 68.21180 |
| Azerbaijan | -100.00000 |
| Macedonia | -84.39432 |
| Rwanda | -79.30289 |
| Saint Lucia | -72.67408 |
| Egypt | -68.95801 |
| Burkina Faso | -66.12854 |
| Honduras | -64.72053 |
| Eritrea | -64.18513 |
| Kuwait | -63.76456 |
| Comoros | -61.38086 |
Downloadable table with full data
Big table for appendix
Missing data in CDR, CFR, HIV
# CDR
df$country[is.na(df$cdr_ihme)][1] "Antigua and Barbuda" "Turkmenistan" "Qatar"
[4] "Bahrain" "Comoros" "Virgin Islands U.S."# CFR
df$country[is.na(df$case_fatality_rate_2015)] [1] "Sudan" "Cambodia"
[3] "Malta" "Antigua and Barbuda"
[5] "Gambia" "Djibouti"
[7] "Turkmenistan" "Qatar"
[9] "Luxembourg" "Bosnia and Herzegovina"
[11] "Canada" "Grenada"
[13] "Japan" "Italy"
[15] "Bahrain" "American Samoa"
[17] "Switzerland" "Ethiopia"
[19] "Belize" "Greece"
[21] "France" "Saint Vincent and the Grenadines"
[23] "Comoros" "Saint Lucia"
[25] "Bermuda" "Virgin Islands U.S." df$country[is.na(df$case_fatality_rate_2014)] [1] "Sudan" "Cambodia"
[3] "Malta" "Antigua and Barbuda"
[5] "Gambia" "Djibouti"
[7] "Turkmenistan" "Qatar"
[9] "Luxembourg" "Bosnia and Herzegovina"
[11] "Canada" "Grenada"
[13] "Japan" "Italy"
[15] "Bahrain" "American Samoa"
[17] "Switzerland" "Ethiopia"
[19] "Belize" "Greece"
[21] "France" "Saint Vincent and the Grenadines"
[23] "Comoros" "Saint Lucia"
[25] "Bermuda" "Virgin Islands U.S." # HIV
df$country[is.na(df$newrel_hivpos)] [1] "Iceland" "Algeria"
[3] "Mauritania" "Austria"
[5] "Turkmenistan" "Qatar"
[7] "Poland" "Hungary"
[9] "Luxembourg" "United Kingdom"
[11] "Bosnia and Herzegovina" "Korea Republic of"
[13] "Germany" "Bulgaria"
[15] "Italy" "Bahrain"
[17] "American Samoa" "Spain"
[19] "Denmark" "Switzerland"
[21] "Greece" "Croatia"
[23] "France" "Sweden"
[25] "Cyprus" "Comoros"
[27] "Virgin Islands U.S." More correlations
Using stand_dif ( (a-b)/(a+b)/2 )
Standardized difference with CDR WHO
Standardized difference with CDR GBD
Standardized difference with CFR
(using variable case_fatality_rate_2015_adjusted)
Comparison of different case fatality rate measures
Alberto asked: can you look at the code and compare case_fatality_rate_2015_adjusted and case_fatality_rate_2015
They are identical except that the adjusted rate has 17 missings, whereas the non adjusted rate has 26 missings.
Standardized difference with CFR
(using variable gb_cfr)
Standardized difference with Reported HIV prevalence
Standardized difference with MDR prevalence
(using reported_mdr)
Standardized difference with MDR prevalence
(using p_mdr_new)
Request A: Correlations with original_stand_diff
CDR WHO
stand_dif_compare(x = df$original_stand_dif,
y = df$gb_c_cdr,
yl = 'CDR WHO')
CDR GBD
stand_dif_compare(x = df$original_stand_dif,
y = df$cdr_ihme,
yl = 'CDR GBD')
HIV prevalence
stand_dif_compare(x = df$original_stand_dif,
y = df$p_hiv_of,
yl = 'HIV prevalence')
MDR prevalence
stand_dif_compare(x = df$original_stand_dif,
y = df$p_mdr_new,
yl = 'MDR prevalence')
Request B: Box plot of association of prevalence survey with stand diff (not original_stand_dif)
Difference using t-test (for p-value)
Welch Two Sample t-test
data: df$stand_dif[df$prevsurvey == 0] and df$stand_dif[df$prevsurvey == 1]
t = -2.1643, df = 21.066, p-value = 0.04207
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.78430145 -0.01573195
sample estimates:
mean of x mean of y
-0.09090786 0.30910884 prev_survey and original_stand_dif
Difference using t-test (for p-value)
Welch Two Sample t-test
data: x$original_stand_dif[x$prevsurvey == 0] and x$original_stand_dif[x$prevsurvey == 1]
t = -1.1965, df = 22.444, p-value = 0.244
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-8.447386 2.261924
sample estimates:
mean of x mean of y
1.027875 4.120606